Comments

This is an awesome series. As a National fan (really), I have been worried about their GB pitcher/poor infield defense ratio, and only now wonder if you might look into the importance of said-D on making these types of pitchers so favourable.

Good work Matthew. I wonder if it wouldn’t be better to do an intraclass correlation for R/27, as each pitcher has a different sample size of BIP and batter’s faced. I realize that’s probably a little bit more complicated than this study would warrant, but it would be more accurate…

They are R^2 values, which describe the amount of variance explained by the correlation. A correlation of .07 means that 7 percent of the variance in FIP is explained by the groundball rate. So yes, these values are too low to make the statement that FIP (or runs allowed) are related to groundball rates.

Given the number of datapoints, it is possible that the result is statistically significant, but I can’t see how the result is meaningful in any way.

The correlation on these graphs is horrible. If I got a graph like this in my physics experiments, I would be forced to say that there is little to no correlation to what I was graphing. I think the conclusion should be that groundball rate has little to no effect on FIP or RA.

Low R-squared values do not imply there is “no relationship.” The correct inference is that ground ball rates of pitchers do not have a lot of predictive power for FIP. This statement is entirely consistent with the statement that on average high ground ball rate pitchers have lover FIPs. The distinction is between means and variances.

I have to agree with both Michael, Notdissertating and Eric. It is difficult to make an inference here on the relationship between ground ball rate and FIP. Matthew, what was the r coefficient (correlation coefficient)? I’m mostly curious. I would ask that readers not make any assumptions based on this data.

Thanks, Notdissertating, for clarifying my post.
The r coefficient should be the square root of the r^2 value. I’m assuming a pearson’s coefficient was used (as the data look normally distributed).

Notdissertating, although this doesn’t address the question of “on average, do high groundball rate pitchers have lower FIP?”, that question has been asked and answered via a comparison of means previously (as Matt indicated at the top of his post); no significant differences between “high” GB and “low” GB pitchers was found (although “high” and “low” were arbitrary terms). To be fair, I don’t think that analysis (as matt points out) was performed properly either. The way to check would be to plot the distribution of GB rates, pick the tails of the distribution to form the “high” and “low” GB pitchers, and then perform a one-tailed t-test on FIP for the two groups.

the r value should be .27 I was wondering if that is what Matthew got. That would mean that there is a correlation about 27 percent of the time. Note:That statement is a crude one and not exactly accurate but it gets my point across. Some of the others could probably do a better job of explaining. Anyway, a .27 r value indicates a weak relationship.

C’mon guys, this isn’t a controlled physics experiment. It’s an uncontrolled real world experiment trying to pick up one effect among at least 10 significant others. There is no “threshold” for what a good R2 should be. It’s ridiculous to categorically say that a “.27 r value indicates a weak relationship”, especially when those speculating can’t even see the significance or standard errors around the slope.

Another thing to understand about “groundball pitchers” and “pitching to contact” … They are generally pitchers with “lesser stuff”. If guys *could* be strikeout pitchers, they would.

So, where a groundball pitcher may pale in comparison to a top 1-2 starter on each staff … by “pitching for groundballs” he may be significantly better than he would be if he pitched “like everyone else. This is where we would like to be able to compare Pitcher A with one approach versus Pitcher A with a different approach, rather than comparing individual pitchers of various types and quality with each other.

Note: By ‘pitching for groundballs’ I am speaking primarily of pounding the strikezone with late breaking/sinking/tailing pitches.

“Pitching to contact” isn’t so much a philosophy choice, but rather the result of a pitcher’s stuff, and the acceptance of what/who he is and using his quality to his advantage. I have coached pitchers whose stuff is so good that they literally cannot “pitch to contact”, due to their combination of velocity and movement. We see similar guys in MLB (relative to their level). It kills me to hear comments suggesting that high strikeout guys should “pitch more to contact” as if the hitters weren’t trying to make contact. *Shrugs*

The KEY is that by focusing on the GB, the GB Pitcher finds a way for him to be successful because his stuff isn’t likely good enough to pitch any other way and survive.

There are some very interesting things in this study. I figured GB pitchers would give up less walks, more hits, and fewer pop-ups. HR’s allowed, i wasn’t sure about. They could be equally susceptible on mistakes, whereas a power pitcher often gets away with “pitching up” b/c their stuff is overwhelming.

Again, we have to remember we’re talking about a group of pitchers whose “stuff rating” is probably around a 6.5 to 7.5, and they (as a group) are rather successful in a league dominated by pitcher’s whose stuff rating would be more in line with 8.0+.

VERY interesting stuff in this groundball series. Thanks for the discussion.

Are the higher slugging percentage results on flyballs caused by or part of the groundball/flyball platoon differential (e.g. flyball hitters generally hit groundball pitchers better than flyball pitchers)?

I’m probably missing something, but looking at the previous post on walks, it seems to me that BG rate has a similar impact on walks and FIP… For whatever reason, in the walk graph, the equation is based on x in integers, while the FIP graph’s equation uses x in decimals (i.e., 25% results in x = 25 for walks but x=0.25 for FIP)… If you put them on the same scale, the slope for walks is 3.72 and for FIP it’s 2.98… The R^2 values are all low, so what am I missing?

I would, but I don’t have a database set up yet that keeps track of ERA. But inferring based off the FIP-RA graph and knowing that as GB% goes up, unearned runs also goes up, I would have confidence that you are correct and the slope would be fairly flat

Hi Matthew. There seems to be a general consensus among us commenters that the R^2 values are too low for the correlation between FIP and GB% to be meaningful. If we’re all wrong for some reason or another, could you please enlighten us? Thanks.